ReviewInternational journal of methods in psychiatric research2025
Estimating Treatment Effect Heterogeneity in Psychiatry: A Review and Tutorial With Causal Forests.
Review in International journal of methods in psychiatric research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Effects of transcutaneous electrical acupoint stimulation versus acupressure on the trajectories of multidimensional adverse reactions to chemotherapy in breast cancer patients: a secondary analysis of a randomized controlled trial.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Trial
- Causal Forests in Practice: Lessons on Detecting Heterogeneous Treatment Effects in a Randomized Controlled Trial of a Healthy Food Subsidy Program in Canada.The Journal of nutrition · 2026Trial
- Estimating and discovering heterogeneous treatment effects using machine learning in epidemiological studies: a practical guide.International journal of epidemiology · 2026Review
- Task-aligned outcome learning in psychiatry: reducing endpoint dilution.Frontiers in psychiatry · 2026Article
- Adjunctive ab-interno goniotomy in chronic angle-closure glaucoma: a retrospective proof-of-concept pilot study using doubly robust learning.Frontiers in ophthalmology · 2026Article
- Causal machine learning methods for understanding land use and land cover change.Landscape ecology · 2026Review
- Article
- Article
- A prediction model for differential resilience to the effects of combat-related stressors in US army soldiers.International journal of methods in psychiatric research · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
backgroundFlexible machine learning tools are increasingly used to estimate heterogeneous treatment effects.
aimsThis paper gives an accessible tutorial demonstrating the use of the causal forest algorithm, available in the R package grf. SUMMARY: We start with a brief non-technical overview of treatment effect estimation methods, focusing on estimation in observational studies; the same techniques can also be applied in experimental studies. We then discuss the logic of estimating heterogeneous effects using the extension of the random forest algorithm implemented in grf. Finally, we illustrate causal forest by conducting a secondary analysis on the extent to which individual differences in resilience to high combat stress can be measured among US Army soldiers deploying to Afghanistan based on information about these soldiers available prior to deployment. We illustrate simple and interpretable exercises for model selection and evaluation, including targeting operator characteristics curves, Qini curves, area-under-the-curve summaries, and best linear projections.
resultsA replication script with simulated data is available at https://github.com/grf-labs/grf/tree/master/experiments/ijmpr.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.